Smart-TCP: An Agentic AI-based Autonomous and Adaptive TCP Protocol
For network protocol designers, this work demonstrates a proof-of-concept for integrating LLMs into TCP control, though it is incremental as it only handles classification and anomaly detection in a simulated environment.
Smart-TCP replaces hard-coded TCP logic with a fast/slow model-assisted decision process using small and large language models, achieving 99.14% action accuracy on anomaly samples and 100% success rate on ideal fast-path sessions.
The Transmission Control Protocol (TCP) relies on deterministic state machines and sequence-number arithmetic to ensure reliable communication. However, hard-coded protocol logic is difficult to adapt to increasingly complex and dynamic network conditions. This paper proposes Smart-TCP, an agentic AI-based transport protocol framework that organizes TCP control logic as a fast/slow model-assisted decision process. Specifically, a feature-aware classifier routes routine segments to a lightweight Small Language Model (SLM) fast path and anomalous or boundary cases to a Large Language Model (LLM) slow path, while an Arithmetic Logic Unit (ALU) handles deterministic sequence- and acknowledgment-number computation. A state module maintains connection state, packet history, and key control variables, enabling protocol decisions over the full session context. We evaluate Smart-TCP through path classification, atomic packet processing, slow-path anomaly response, and end-to-end session interaction. Experimental results show that Smart-TCP achieves 99.14% action accuracy on 700 slow-path anomaly and fallback samples, and a 100% full-lifecycle success rate over 300 ideal fast-path end-to-end session windows. These results suggest that decoupling model-based protocol reasoning from deterministic arithmetic improves the reliability of model-driven TCP control.